Various attempts to monitor the health and make fault diagnoses of machines have been attempted. Conventionally, this is done by human experts after abnormal behaviour, such as increased vibration level, has been observed. The experts may in cases be supported by expert systems.
The fault diagnosis may include analysing vibration and process data from critical machines to determine whether the specific machine components show a tendency of imbalance, misalignment, cracks, wear or other faults.
As regards the use of expert systems, the results from such systems may merely indicate the association of a symptom with the likelihood of a potential fault, but does not predict the future development of this fault. Furthermore, expert systems are sensitive to boundary conditions of the collected data and knowledge, and may even indicate faults, which would possibly not occur in practice. In addition, such a method, when monitoring the many machine components, soon arrives at a point where the amount of data to be analysed reach the limit of the capacity of the computer executing the algorithms of the expert system.
Expert systems and other types of automatic fault detection systems also require training, and the difficulty of training an expert system for the many different types of fault and indicators thereof maybe prohibitive.
There accordingly remains a need for an improved automatic machinery fault diagnostic method and apparatus.